{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yellowbrick Feature Importance Examples \n",
    "\n",
    "This notebook is a sample of the feature importance examples that yellowbrick provides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.insert(0, \"../..\")\n",
    "\n",
    "import importlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yellowbrick\n",
    "import yellowbrick as yb\n",
    "from yellowbrick.features.importances import FeatureImportances\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import manifold, datasets\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "\n",
    "mpl.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Iris Datasets for Example Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_iris, y_iris = datasets.load_iris(True)\n",
    "X_iris_pd = pd.DataFrame(X_iris, columns=['f1', 'f2', 'f3', 'f4'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression with Mean of Feature Importances\n",
    "\n",
    "*Should we normalize relative to maximum value or maximum absolute value?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h0KFD6tevn7Kzs+Xr6+vodVZWlmbOnKl9+/bp66+/1tSpU5WTk6OqVavqlltukZOTU75x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\ngQYAAFjv/wG0dG2BMAIrUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd04569f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression())\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lqch1UBQ/Pz/n3oWTJ0+qe/fucjgc8vPzc67ra9euadasWfrmm2+0b98+zZgxQ9nZ2apS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\nAGA9ggYAAFiPoAEAANb7P47tpRr/nulTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd044ece518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), relative=False)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHFG/fv2Um5srf39/Z1/n5ORo+vTp+vHHH/XNN99o8uTJcjgcqlatmm6++Wa5uLhc0u49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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd046b57860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), absolute=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AKJFiA80XX3whLy8vjRw5UhcuXNDw4cMJNAAA4Fel2EDTs2dPx8+xsbGqX79+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\n/wfyj6bxcE5ooAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd046bf4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), relative=False, absolute=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression with Stacked Feature Importances\n",
    "\n",
    "*Need to decide how to scale scale feature importance when `relative=True`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kFbgNCuLr6+s8+3T8+HH17t1bmZmZ8vX1dW7rjIwMTZ8+XYcPH9ZXX32lyZMnKysrSxUq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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd046bc57b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), stack=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EPAAAAMsQ8AAAACzjKM5OU6dO1d69e+Xh4aHRo0crMDDwRtcFAACAEioy4O3a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd04563e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), stack=True, relative=False)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S0tIkqcRlUJKAgADn0af9+/erd+/eys3NVUBAgHNZ5+TkaOrUqdq9e7e++uorTZw4UXl5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\ntgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd04a903dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), stack=True, absolute=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pp5/me3nN4XDo0qVLklTgGBQkODjYNft04sQJde3aVU6nU8HBwa6xzs7O1owZM3TkyBHt\n3LlTU6dOVU5OjsqXL69HHnlEXl5eN/T79NNPa9myZZKuzmJs2rSp0NmydevWadeuXfryyy9dl7G+\n/PLLIoc76eqlxPvuu8/1hzopKUl///vflZ6ersTERD344IOqUKGCzpw5o23btuU5T66N5e9//3ud\nP39eiYmJcjqd+s///M8Cx++f//ynMjIyJF09XmvWrFFSUpL69++v1NRUeXt7q0GDBjcdp4CAAB07\ndkw5OTlKSkrSf//3f7vqLkr7/Dz22GPavHmzcnJyFB8f7+q3qBwOh1q0aOE6hj///LP279+v5s2b\nKygoSPv27VNSUlK+H0P03Xff6W9/+5syMzPl6+urwMBAV/25x/qaWrVq6b777nNdWl25cqUmTZp0\n09qefPJJ/fGPf9TixYslFfxaDQoKcvW5devWfMNoQefNggULtHLlSklXZwBr1qwpLy+vfJfndjuv\nA8BGzOChQFOnTtXkyZO1YsUKSdKf/vQnVa9eXZ06ddK6devUtm1bPfTQQxoxYoSGDh2q6dOnq3//\n/po9e7ZatWql7du3a9iwYfrLX/6igIAARUVF5buv+vXra8iQIYqKilJOTo78/f01depUtz6fKlWq\nqEuXLkpJSdGECRNUqVIlBQUFadCgQerVq5dycnJUr149TZky5abtW7Rooffff1/dunXT22+/ne8Y\n5H7n8PVGjRqlMWPGKDg4WBUqVNDs2bNVrlw5DR8+XFOnTlVYWJgkqVWrVnrkkUeUnZ2tdevWKSws\nTL6+vqpSpYpmzJhxQ78jRozQlClTFB4eLm9vbw0ePFhBQUFuGbeCeHl5ac6cOZoyZYrmzZsnb29v\n9e/fX+XLl1dkZKSGDRum4OBgBQYGKjo6Wi+++KLef//9PGO5atUqdevWTc8++6xq1KihLl266Ntv\nv73p/kJDQ3XixAlFRERIujrr9tprr6lKlSpq1aqVunXrJh8fH5UpU0avvfbaDe3Dw8P12WefKSQk\nRA899JA6dOighISEIrfPT48ePbRv3z6FhISoTp066tixo1JSUm66bXx8vMLDw/Ms++yzz/Tqq69q\nwoQJWr16tcqUKaPp06erevXqql69uiIiIhQREaHq1aurY8eON7wDtE6dOqpZs6Y6deqkMmXKqHz5\n8q57K8PCwjRixAi9/PLLeY7bvHnzNHr0aM2ZM0e///3vNXPmzHyf34gRI9SnTx917969wNfqqFGj\n9Morr2jdunVq3bq1GjZseNOgXNB506VLF0VHR2vRokXy8vJSgwYN1KVLF507d+6my3P/g8BTrwOg\npPEyxhhPFwHcDXXr1tX27dvd9nl/wPWu3YogSTExMXI6nRo3bpzb+962bZvmzZtX7B8ofrty19qt\nWzcNHTpUISEhHq4KKF24RAsAbrBlyxZ169ZNmZmZSktL0/bt29WwYUO39J2UlKSnnnrK9TmLGzZs\ncFvf7hYTE+OazTt58qS+//5717vJAdw9XKIFADdo06aNtm/frg4dOsjb21tt2rS54TLs7apSpYqG\nDx+ufv36ycvLSw899JBGjx7tlr7drX///ho9erRCQ0Pl7e2tSZMmMWsOeACXaAEAACzDJVoAAADL\nuPUSbU5OjtLS0lSmTJlb+ngBAACAglz7QO0KFSrI25v5qcK4NeClpaXp2LFj7uwSAADApU6dOnm+\nhQU359aAV6ZMGUlXB9/X19edXaOIDh8+zDvWPIxj4FmMv2cx/p5n6zHIzMzUsWPHXFkDBXNrwLt2\nWdbX1/emX86Ou4Ox9zyOgWcx/p7F+HuezceAW8CKhovYAAAAliHgAQAAWIaABwAAYBkCHgAAgGUI\neAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGS9jjHFXZ1euXNHhw4fV\n5Z/HFZ+W5a5uAVznvYgjni6hWNUPn+3pEgDcoiap2cXa/7WMERgYaPV37boLM3gAAACWIeABAABY\nhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAAgGUIeAAAAJYh4AEAAFiGgAcAAGAZ\nAh4AAIBlCHgAAACWIeABAABYhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAAgGUI\neAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGUdRNpo1a5YOHDig7Oxs\nDR48WO3bty/uugAAAHCbCg14e/bs0fHjx7Vs2TIlJycrIiKCgAcAAFCCFRrwmjRpoqCgIElSpUqV\nlJGRIafTKR8fn2IvDgAAALeu0HvwfHx8VL58eUnSihUr1Lp1a8IdAABACVake/AkafPmzVq5cqUW\nL15cnPUAAADgDhUp4O3YsUMLFy7Uu+++q3vuuae4awIAAMAdKDTgXbp0SbNmzdKSJUvk5+d3N2oC\nAADAHSg04K1fv17JyckaPny4a1lMTIxq1KhRrIUBAADg9hQa8Lp3767u3bvfjVoAAADgBnyTBQAA\ngGUIeAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGQIeAACAZQh4AAAA\nliHgAQAAWIaABwAAYBkCHgAAgGUIeAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeABAABY\nhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAAgGUcxdHpyfERKlu2bHF0jUIcOHBA\njRs39nQZpRrHwA1S/3HbTRl/z2L8PY9jAIkZPAAAAOsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADL\nEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACxD\nwAMAALCMozg6rf3aGsWnZRVH1yiKj49Kkt6LOOLhQkqvQztXeLqEfNUPn+3pEordPk8XUMox/ndf\nk9RsT5eAEoYZPAAAAMsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPAADA\nMgQ8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADL\nEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACzj\nKGyDjIwMjR07VomJibpy5Yr++te/qm3btnejNgAAANyGQgPe1q1bFRgYqBdeeEFnzpzRgAEDCHgA\nAAAlWKEBr2PHjq6f4+PjVa1atWItCAAAAHem0IB3TWRkpH799VctXLiwOOsBAADAHSrymyw+/fRT\nvfXWWxo1apSMMcVZEwAAAO5AoQHv8OHDio+PlyTVq1dPTqdTSUlJxV4YAAAAbk+hAW///v1avHix\nJCkhIUHp6emqXLlysRcGAACA21NowIuMjFRSUpJ69uypQYMGadKkSfL25uPzAAAASqpC32RRrlw5\nvfHGG3ejFgAAALgBU3EAAACWIeABAABYhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkC\nHgAAgGUIeAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeABAABYxuHpAgAAAHCjGTNm6ODB\ng/Ly8tK4ceMUFBRU5LYEPAAAgEL4vBLn1v6cb0QVuP6rr77STz/9pGXLlunEiROKjo7WihUritw/\nl2gBAABKmN27dyskJESS9PDDDyslJUWpqalFbk/AAwAAKGESEhJUuXJl12N/f3+dP3++yO0JeAAA\nACWMMeaGx15eXkVuT8ADAAAoYapVq6aEhATX43Pnzqlq1apFbk/AAwAAKGFatGihjRs3SpKOHj2q\ngIAAVaxYscjteRctAABACdOoUSPVr19fkZGR8vLy0uTJk2+pPQEPAACgEIV9rElxGDly5G235RIt\nAACAZQh4AAAAliHgAQAAWKZY7sE7OT5CZcuWLY6uUYgDBw6ocePGni6jVCvxxyD1H56uoFiV+PG3\nHOMPlAzM4AEAAFiGgAcAAGAZAh4AAEAJdezYMYWEhGjp0qW31I7PwQMAACjEkp1j3dpfv5aF3w+d\nnp6uadOmqVmzZrfcPzN4AAAAJZCvr68WLVqkgICAW27LDB4AAEAJ5HA45HDcXlRjBg8AAMAyBDwA\nAADLEPAAAAAswz14AAAAJdDhw4cVExOjM2fOyOFwaOPGjYqNjZWfn1+hbQl4AAAAhSjKx5q4W2Bg\noOLi4m6rLZdoAQAALEPAAwAAsAwBDwAAwDIEPAAAAMsUy5ssar+2RvFpWcXRNYri46OeruCWvBdx\nxNMluN2hnSs8XUKpdrPxrx8+2wOVlE77PF3AXdAkNdvTJQAFYgYPAADAMnxMCgAAQAk0a9YsHThw\nQNnZ2Ro8eLDat29f5LYEPAAAgELsq+jeyFTYZf49e/bo+PHjWrZsmZKTkxUREUHAAwAA+C1r0qSJ\ngoKCJEmVKlVSRkaGnE6nfHx8itSee/AAAABKGB8fH5UvX16StGLFCrVu3brI4U5iBg8AAKDE2rx5\ns1auXKnFixffUjsCHgAAQAm0Y8cOLVy4UO+++67uueeeW2pLwAMAAChhLl26pFmzZmnJkiXy8/O7\n5fYEPAAAgBJm/fr1Sk5O1vDhw13LYmJiVKNGjSK1J+ABAAAU4m5/e0n37t3VvXv3227Pu2gBAAAs\nQ8ADAACwDAEPAADAMgQ8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACxDwAMAALAM\nAQ8AAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyxQp4B07dkwhISFaunRpcdcDAACA\nO1RowEtPT9e0adPUrFmzu1EPAAAA7lChAc/X11eLFi1SQEDA3agHAAAAd8hR6AYOhxyOQjcDAABA\nCcGbLAAAACxDwAMAALAMAQ8AAMAyhd5cd/jwYcXExOjMmTNyOBzauHGjYmNj5efndzfqAwAAwC0q\nNOAFBgYqLi7ubtQCAAAAN+ASLQAAgGUIeAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgAAACWIeAB\nAABYhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAAgGUIeAAAAJYh4AEAAFiGgAcA\nAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGQIeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAA\ngGUIeAAAAJZxFEenJ8dHqGzZssXRNQpx4MABNW7c2NNllGocA8/Kd/xT/3H3iymFOP+BkoEZPAAA\nAMsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyxDwAAAA\nLEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACzjKI5Oa7+2RvFpWcXR9R17L+KIp0soUP3w2Xfcxz43\n1IE7Y9MxaJKa7ekSAAC3iBk8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACxDwAMA\nALAMAQ8AAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyxDwAAAALEPAAwAAsAwBDwAA\nwDIEPAAAAMsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAA\nyxDwAAAALEPAAwAAsIyjKBvNmDFDBw8elJeXl8aNG6egoKDirgsAAAC3qdCA99VXX+mnn37SsmXL\ndOLECUVHR2vFihV3ozYAAADchkIv0e7evVshISGSpIcfflgpKSlKTU0t9sIAAABwewoNeAkJCapc\nubLrsb+/v86fP1+sRQEAAOD2FRrwjDE3PPby8iq2ggAAAHBnCg141apVU0JCguvxuXPnVLVq1WIt\nCgAAALev0IDXokULbdy4UZJ09OhRBQQEqGLFisVeGAAAAG5Poe+ibdSokerXr6/IyEh5eXlp8uTJ\nd6MuAAAA3KYifQ7eyJEji7sOAAAAuAnfZAEAAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGQIe\nAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAAgGUIeAAAAJYh4AEAAFiGgAcAAGAZAh4AAIBlCHgA\nAACWIeABAABYhoAHAABgGQJ9h5ieAAAKPklEQVQeAACAZQh4AAAAliHgAQAAWIaABwAAYBkCHgAA\ngGUIeAAAAJYh4AEAAFiGgAcAAGAZR3F0enJ8hMqWLVscXdsv9R931PzAgQNq3Lixm4rB7eAYAAA8\njRk8AAAAyxDwAAAALEPAAwAAsAwBDwAAwDIEPAAAAMsQ8AAAACxDwAMAALAMAQ8AAMAyBDwAAADL\nEPAAAAAsQ8ADAACwDAEPAADAMgQ8AAAAyzjc2ZkxRpKUmZnpzm5xi65cueLpEko9joFnMf6exfh7\nno3H4Fq2uJY1UDAv48aRunTpko4dO+au7gAAAPKoU6eO7rnnHk+XUeK5NeDl5OQoLS1NZcqUkZeX\nl7u6BQAApZwxRllZWapQoYK8vbnDrDBuDXgAAADwPCIwAACAZQh4AAAAliHgAQAAWIaABwAAYBm3\nBbwZM2aoe/fuioyM1DfffOOubnELjh07ppCQEC1dutTTpZRKs2bNUvfu3dWtWzdt2rTJ0+WUOhkZ\nGXr55ZfVu3dvPffcc9q6daunSyqVLl++rHbt2mn16tWeLqVUOXz4sFq3bq2oqChFRUVp2rRpni4J\nHuaWDzr+6quv9NNPP2nZsmU6ceKEoqOjtWLFCnd0jSJKT0/XtGnT1KxZM0+XUirt2bNHx48f17Jl\ny5ScnKyIiAi1b9/e02WVKlu3blVgYKBeeOEFnTlzRgMGDFDbtm09XVap89Zbb8nPz8/TZZQ66enp\nCgsL0/jx4z1dCkoItwS83bt3KyQkRJL08MMPKyUlRampqapYsaI7ukcR+Pr6atGiRVq0aJGnSymV\nmjRpoqCgIElSpUqVlJGRIafTKR8fHw9XVnp07NjR9XN8fLyqVavmwWpKp5MnT+rEiRNq06aNp0sp\nddLS0jxdAkoYt1yiTUhIUOXKlV2P/f39df78eXd0jSJyOBwqV66cp8sotXx8fFS+fHlJ0ooVK9S6\ndWvCnYdERkZq5MiRGjdunKdLKXViYmI0duxYT5dRKqWnp+vAgQP6y1/+ol69emnPnj2eLgke5pYZ\nvOs/K9kYwzdZoFTavHmzVq5cqcWLF3u6lFLr008/1bfffqtRo0bps88+43fRXbJ27Vo1bNhQ//Zv\n/+bpUkqlRx55RC+++KLatWunH374Qf3799emTZvk6+vr6dLgIW4JeNWqVVNCQoLr8blz51S1alV3\ndA38ZuzYsUMLFy7Uu+++y/ckesDhw4fl7++v6tWrq169enI6nUpKSpK/v7+nSysVtm3bplOnTmnb\ntm369ddf5evrq/vuu0/Nmzf3dGmlQu3atVW7dm1J0oMPPqiqVavq7NmzBO5SzC0Br0WLFoqNjVVk\nZKSOHj2qgIAA7r9DqXLp0iXNmjVLS5Ys4QZzD9m/f7/OnDmj8ePHKyEhQenp6XluHUHxmjdvnuvn\n2NhY3X///YS7u2jlypVKT09Xnz59dP78eSUmJnIfainnloDXqFEj1a9fX5GRkfLy8tLkyZPd0S1u\nweHDhxUTE6MzZ87I4XBo48aNio2NJWzcJevXr1dycrKGDx/uWhYTE6MaNWp4sKrSJTIyUuPHj1fP\nnj11+fJlTZo0iS8kR6kRGhqqkSNHauPGjcrMzNSUKVO4PFvKeZnrb6ADAADAbxr/vAUAALAMAQ8A\nAMAyBDwAAADLEPAAAAAsQ8ADAACwDAEPQJGkpKSoU6dO+utf/yqn06kePXqoe/fu+uabbzRt2rR8\n23377bcFri9IRkaGNm3adLslu0RFRWnXrl133A8A/Fa45XPwANjv2LFj+t3vfqc333xT8fHx+umn\nn1yhKSgoKN929erV08SJE29rn0ePHtWmTZvUvn3722oPAKUVM3hAKfDmm2+qW7dueu6557R06VJJ\n0g8//KA+ffooKipKPXr00P79+yVJFy9e1PDhw9W3b1/16NFDn3/+udLS0jRt2jR9//33eumllxQd\nHa2UlBRFRUVp586d6tGjhyTpxx9/VFRUlHr16qUBAwbo7Nmz2rt3r2v9L7/8osGDB6tfv37q1auX\nKyCOHTtWc+bM0ZAhQxQWFqZFixbp8uXLGj9+vHbt2qVZs2a5novT6VTLli119uxZ17L27dvr5MmT\n+uKLL9S9e3dFRUWpZ8+eOn36dJ5xyF3Ltf2uWLFC0tUPq+7Zs6f69u2rYcOGKTk52d2HAQDuGgIe\nYLn9+/dr27ZtWr58uT766CNt3bpVKSkpmj59unr06KG4uDhNmTJFY8aMkXT1K6datWqlDz74QO+9\n957mz5+vK1euaNy4capTp47+4z/+Q9OnT1eVKlUUFxenMmXKuPY1efJkDRw4UB999JE6deqkDRs2\n5KllypQp6t+/v5YsWaJ58+ZpwoQJys7OliSdOnVKCxcu1OLFi7Vw4UKVK1dOgwYNUvPmzTV69GhX\nHz4+PurQoYM2btwo6eq3uFSsWFG1a9dWSkqK5s6dq7i4OD399NP66KOPijRG8fHxWrhwoZYsWaIP\nPvhATzzxhN5+++07GncA8CQu0QKWO3jwoBo3biwfHx/5+Pjovffecy2fO3euJKlu3bpKTU1VUlKS\n9u7dq0OHDmnt2rWSJIfDccNMWH6++eYbNW3aVJLUtWtXSVdnza7Zu3ev0tLStGDBAlffiYmJkuRq\nd//99ys1NVVOpzPf/XTu3FkxMTHq06eP1q9fry5dukiS/P39NWbMGBljdP78eT3++ONFqvvrr7/W\n+fPnNXDgQElSZmamatasWaS2AFASEfAAy3l5eelm30jo5eV102W+vr6aPHmyHnvssTzrcge1guTk\n5OS7ztfXV7GxsapSpcoN6xyOvL+OCvoWxaCgICUmJurcuXP64osv9MknnygrK0sjRozQmjVrVKtW\nLS1dulSHDx/O0+7655yVleWqKygoiFk7ANbgEi1guccff1y7d+9WVlaWsrOzFRUVpXPnzqlBgwba\nuXOnpKtvZvDz81PlypXVuHFj16XVy5cva8qUKa7LqIVp1KiRduzYIenqPW1z5szJsz5330lJSZox\nY0aB/Xl7e+vKlSs3XffMM8/ozTffVK1atVS1alWlpaUpJydH1atX15UrV7RlyxZlZmbmaVOxYkWd\nPXtWxhhlZGTo4MGDkqTHHntM33zzjc6fPy9J2rBhgzZv3lyk5wwAJREzeIDlHn/8cbVv3169evWS\ndDUYBQQEaOLEiZo8ebI++eQTZWdnu97I8NJLL2nChAnq0aOHMjMz1b179xtm1/IzceJETZw4UR9/\n/LEcDodmzJihn3/+2bV+/PjxmjRpktatW6fMzEwNHTq0wP4ee+wxzZ49W9HR0Zo5c2aedZ07d1bH\njh0VExMjSfLz89Ozzz6r559/XjVq1NDAgQM1evToPPcBPvLII6pbt64iIiL0wAMPuC7hVqtWTePH\nj9fgwYP1u9/9TuXKlXP1CwC/RV6moOsgAAAA+M3hEi0AAIBlCHgAAACWIeABAABYhoAHAABgGQIe\nAACAZQh4AAAAliHgAQAAWIaABwAAYJn/B5B5/CZlyZE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd04a901f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), stack=True, relative=False, absolute=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Digits Datasets for Example Code\n",
    "\n",
    "*Should we add an option to show only top n features?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_digits, y_digits = datasets.load_digits(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GXbt2AShaFD9//nw8ffoUL168QOvWrWFsbIy0tDQcPHhQ8ctbX18fWVlZEAQBxsbG0NPT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\ntUyagC67OL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd044e08400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), stack=True, relative=True)\n",
    "viz.fit(X_digits, y_digits)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SzHX8PRFoUGwzZszQrl27FBQUpP79++vaa6/VVVddpT59+ig+Pl7du3fX2LFjFRoaqhMn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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0452f0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LinearRegression())\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SzHX8PRFoUGwzZszQrl27FBQUpP79++vaa6/VVVddpT59+ig+Pl7du3fX2LFjFRoaqhMn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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd044e24f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LinearRegression(), stack=True)\n",
    "viz.fit(X_iris, y_iris)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "importlib.reload(yellowbrick.features.importances)\n",
    "from yellowbrick.features.importances import FeatureImportances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c3d116dIlScpyDLLi4+PjmH06ceKEOnbsqJSUFPn4+DjGOjk5WRMnTtThw4e1e/dujRs3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0455b55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = FeatureImportances(LogisticRegression(), relative=False, absolute=False, stack=True)\n",
    "viz.fit(X_pd, y)\n",
    "viz.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
